Introduction¶

Airbnb is an online platform that allows individuals to rent out their homes or parts of their homes to travelers on a short-term basis. The name “Airbnb” stands for “Air Bed and Breakfast” reflecting the company’s humble beginnings when the founders rented out air mattresses in their apartment to attendees of a local conference.¶

The following Project include data for a particular city [Boston]. The analysis will focus on factors such as price, availability, location, and property type to identify trends and patterns in the demand for Airbnb listings in the city¶

My Libraries¶

In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px

Data Wrangling¶

In [2]:
df=pd.read_csv('D:/sql/Data set/Boston/listings_1.csv')
df.head()
Out[2]:
id listing_url scrape_id last_scraped source name description neighborhood_overview picture_url host_id ... review_scores_communication review_scores_location review_scores_value license instant_bookable calculated_host_listings_count calculated_host_listings_count_entire_homes calculated_host_listings_count_private_rooms calculated_host_listings_count_shared_rooms reviews_per_month
0 5.216497e+07 https://www.airbnb.com/rooms/52164968 2.020000e+13 6/22/2023 previous scrape Home in Boston · ★4.90 · 1 bedroom · 1 bed · 1... Whether you want to spend a weekend or a coupl... We're located in a lovely, safe and quiet neig... https://a0.muscache.com/pictures/miso/Hosting-... 400176914 ... 4.94 4.74 4.81 NaN f 2 1 1 0 1.45
1 5.107200e+07 https://www.airbnb.com/rooms/51072005 2.020000e+13 6/22/2023 city scrape Place to stay in Boston · ★4.80 · 1 bedroom · ... ------PERKS------<br />- PRIVATE DRIVEWAY<br /... We're located in a lovely, safe and quiet neig... https://a0.muscache.com/pictures/miso/Hosting-... 400176914 ... 4.89 4.72 4.63 STR-458877 f 2 1 1 0 7.03
2 3.344684e+07 https://www.airbnb.com/rooms/33446838 2.020000e+13 6/21/2023 city scrape Home in Boston · 1 bedroom · 1 bed · 1 shared ... 3 min walk to the T Blue Line Orient Heights 3... Safe; no Crime; Great professional families https://a0.muscache.com/pictures/96d0afc0-bf64... 22402141 ... 5.00 5.00 5.00 NaN f 1 0 1 0 0.02
3 1.861231e+07 https://www.airbnb.com/rooms/18612312 2.020000e+13 6/22/2023 city scrape Home in Boston · 1 bedroom · 1 bed · 1 bath Apartment located in East Boston. Renovated Ki... NaN https://a0.muscache.com/pictures/8e8f232b-eade... 129278480 ... NaN NaN NaN NaN f 5 0 5 0 NaN
4 7.990000e+17 https://www.airbnb.com/rooms/798533622805798105 2.020000e+13 6/22/2023 city scrape Rental unit in Boston · ★4.57 · 1 bedroom · 1 ... Peaceful and comfortable studio located in Eas... NaN https://a0.muscache.com/pictures/miso/Hosting-... 193540040 ... 4.93 4.57 4.36 STR-518875 t 2 2 0 0 3.21

5 rows × 75 columns

Data Preparation & Cleaning¶

In [3]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3973 entries, 0 to 3972
Data columns (total 75 columns):
 #   Column                                        Non-Null Count  Dtype  
---  ------                                        --------------  -----  
 0   id                                            3973 non-null   float64
 1   listing_url                                   3973 non-null   object 
 2   scrape_id                                     3973 non-null   float64
 3   last_scraped                                  3973 non-null   object 
 4   source                                        3973 non-null   object 
 5   name                                          3973 non-null   object 
 6   description                                   3954 non-null   object 
 7   neighborhood_overview                         2571 non-null   object 
 8   picture_url                                   3973 non-null   object 
 9   host_id                                       3973 non-null   int64  
 10  host_url                                      3973 non-null   object 
 11  host_name                                     3973 non-null   object 
 12  host_since                                    3973 non-null   object 
 13  host_location                                 3168 non-null   object 
 14  host_about                                    2794 non-null   object 
 15  host_response_time                            3492 non-null   object 
 16  host_response_rate                            3492 non-null   object 
 17  host_acceptance_rate                          3484 non-null   object 
 18  host_is_superhost                             2670 non-null   object 
 19  host_thumbnail_url                            3973 non-null   object 
 20  host_picture_url                              3973 non-null   object 
 21  host_neighbourhood                            3852 non-null   object 
 22  host_listings_count                           3973 non-null   int64  
 23  host_total_listings_count                     3973 non-null   int64  
 24  host_verifications                            3973 non-null   object 
 25  host_has_profile_pic                          3973 non-null   object 
 26  host_identity_verified                        3973 non-null   object 
 27  neighbourhood                                 2571 non-null   object 
 28  neighbourhood_cleansed                        3973 non-null   object 
 29  neighbourhood_group_cleansed                  0 non-null      float64
 30  latitude                                      3973 non-null   float64
 31  longitude                                     3973 non-null   float64
 32  property_type                                 3973 non-null   object 
 33  room_type                                     3973 non-null   object 
 34  accommodates                                  3973 non-null   int64  
 35  bathrooms                                     0 non-null      float64
 36  bathrooms_text                                3972 non-null   object 
 37  bedrooms                                      2417 non-null   float64
 38  beds                                          3900 non-null   float64
 39  amenities                                     3973 non-null   object 
 40  price                                         3973 non-null   object 
 41  minimum_nights                                3973 non-null   int64  
 42  maximum_nights                                3973 non-null   int64  
 43  minimum_minimum_nights                        3973 non-null   int64  
 44  maximum_minimum_nights                        3973 non-null   int64  
 45  minimum_maximum_nights                        3973 non-null   int64  
 46  maximum_maximum_nights                        3973 non-null   int64  
 47  minimum_nights_avg_ntm                        3973 non-null   float64
 48  maximum_nights_avg_ntm                        3973 non-null   float64
 49  calendar_updated                              0 non-null      float64
 50  has_availability                              3973 non-null   object 
 51  availability_30                               3973 non-null   int64  
 52  availability_60                               3973 non-null   int64  
 53  availability_90                               3973 non-null   int64  
 54  availability_365                              3973 non-null   int64  
 55  calendar_last_scraped                         3973 non-null   object 
 56  number_of_reviews                             3973 non-null   int64  
 57  number_of_reviews_ltm                         3973 non-null   int64  
 58  number_of_reviews_l30d                        3973 non-null   int64  
 59  first_review                                  2911 non-null   object 
 60  last_review                                   2911 non-null   object 
 61  review_scores_rating                          2911 non-null   float64
 62  review_scores_accuracy                        2903 non-null   float64
 63  review_scores_cleanliness                     2904 non-null   float64
 64  review_scores_checkin                         2902 non-null   float64
 65  review_scores_communication                   2904 non-null   float64
 66  review_scores_location                        2902 non-null   float64
 67  review_scores_value                           2902 non-null   float64
 68  license                                       1888 non-null   object 
 69  instant_bookable                              3973 non-null   object 
 70  calculated_host_listings_count                3973 non-null   int64  
 71  calculated_host_listings_count_entire_homes   3973 non-null   int64  
 72  calculated_host_listings_count_private_rooms  3973 non-null   int64  
 73  calculated_host_listings_count_shared_rooms   3973 non-null   int64  
 74  reviews_per_month                             2911 non-null   float64
dtypes: float64(19), int64(21), object(35)
memory usage: 2.3+ MB
In [4]:
pd.DataFrame(df.apply(lambda col: len(col.unique())),columns=["Unique Values Count"])
Out[4]:
Unique Values Count
id 2847
listing_url 3973
scrape_id 1
last_scraped 2
source 2
... ...
calculated_host_listings_count 36
calculated_host_listings_count_entire_homes 33
calculated_host_listings_count_private_rooms 21
calculated_host_listings_count_shared_rooms 4
reviews_per_month 627

75 rows × 1 columns

The data has too many columns we have to divide it to two sperated data to deal with it¶

Catigorical data & Numerical data¶

In [5]:
cat_df=df.select_dtypes(include=['object']).columns.tolist()
cat_df
Out[5]:
['listing_url',
 'last_scraped',
 'source',
 'name',
 'description',
 'neighborhood_overview',
 'picture_url',
 'host_url',
 'host_name',
 'host_since',
 'host_location',
 'host_about',
 'host_response_time',
 'host_response_rate',
 'host_acceptance_rate',
 'host_is_superhost',
 'host_thumbnail_url',
 'host_picture_url',
 'host_neighbourhood',
 'host_verifications',
 'host_has_profile_pic',
 'host_identity_verified',
 'neighbourhood',
 'neighbourhood_cleansed',
 'property_type',
 'room_type',
 'bathrooms_text',
 'amenities',
 'price',
 'has_availability',
 'calendar_last_scraped',
 'first_review',
 'last_review',
 'license',
 'instant_bookable']
In [6]:
df_object=df.loc[:,cat_df]
df_object.head()
Out[6]:
listing_url last_scraped source name description neighborhood_overview picture_url host_url host_name host_since ... room_type bathrooms_text amenities price has_availability calendar_last_scraped first_review last_review license instant_bookable
0 https://www.airbnb.com/rooms/52164968 6/22/2023 previous scrape Home in Boston · ★4.90 · 1 bedroom · 1 bed · 1... Whether you want to spend a weekend or a coupl... We're located in a lovely, safe and quiet neig... https://a0.muscache.com/pictures/miso/Hosting-... https://www.airbnb.com/users/show/400176914 TuanAnh 5/5/2021 ... Private room 1 private bath ["Body soap", "Bathtub", "Hair dryer", "Smoke ... $130.00 t 6/22/2023 9/19/2021 7/4/2022 NaN f
1 https://www.airbnb.com/rooms/51072005 6/22/2023 city scrape Place to stay in Boston · ★4.80 · 1 bedroom · ... ------PERKS------<br />- PRIVATE DRIVEWAY<br /... We're located in a lovely, safe and quiet neig... https://a0.muscache.com/pictures/miso/Hosting-... https://www.airbnb.com/users/show/400176914 TuanAnh 5/5/2021 ... Entire home/apt 1 bath ["Cooking basics", "Hair dryer", "Smoke alarm"... $150.00 t 6/22/2023 9/12/2021 6/8/2023 STR-458877 f
2 https://www.airbnb.com/rooms/33446838 6/21/2023 city scrape Home in Boston · 1 bedroom · 1 bed · 1 shared ... 3 min walk to the T Blue Line Orient Heights 3... Safe; no Crime; Great professional families https://a0.muscache.com/pictures/96d0afc0-bf64... https://www.airbnb.com/users/show/22402141 Mirian 10/11/2014 ... Private room 1 shared bath ["Indoor fireplace", "Cooking basics", "Washer... $60.00 t 6/21/2023 8/18/2019 8/18/2019 NaN f
3 https://www.airbnb.com/rooms/18612312 6/22/2023 city scrape Home in Boston · 1 bedroom · 1 bed · 1 bath Apartment located in East Boston. Renovated Ki... NaN https://a0.muscache.com/pictures/8e8f232b-eade... https://www.airbnb.com/users/show/129278480 Jay 5/7/2017 ... Private room 1 bath ["Washer", "Hair dryer", "Smoke alarm", "Heati... $50.00 t 6/22/2023 NaN NaN NaN f
4 https://www.airbnb.com/rooms/798533622805798105 6/22/2023 city scrape Rental unit in Boston · ★4.57 · 1 bedroom · 1 ... Peaceful and comfortable studio located in Eas... NaN https://a0.muscache.com/pictures/miso/Hosting-... https://www.airbnb.com/users/show/193540040 Luciano 6/4/2018 ... Entire home/apt 1 bath ["Body soap", "Free street parking", "Essentia... $140.00 t 6/22/2023 2/12/2023 6/19/2023 STR-518875 t

5 rows × 35 columns

In [7]:
num_df=df.select_dtypes(['float64','int64']).columns.tolist()
In [8]:
df_number=df.loc[:,num_df]
df_number.head()
Out[8]:
id scrape_id host_id host_listings_count host_total_listings_count neighbourhood_group_cleansed latitude longitude accommodates bathrooms ... review_scores_cleanliness review_scores_checkin review_scores_communication review_scores_location review_scores_value calculated_host_listings_count calculated_host_listings_count_entire_homes calculated_host_listings_count_private_rooms calculated_host_listings_count_shared_rooms reviews_per_month
0 5.216497e+07 2.020000e+13 400176914 2 2 NaN 42.392280 -71.006760 2 NaN ... 4.97 5.00 4.94 4.74 4.81 2 1 1 0 1.45
1 5.107200e+07 2.020000e+13 400176914 2 2 NaN 42.391317 -71.007146 4 NaN ... 4.84 4.95 4.89 4.72 4.63 2 1 1 0 7.03
2 3.344684e+07 2.020000e+13 22402141 1 3 NaN 42.390720 -71.004560 1 NaN ... 5.00 5.00 5.00 5.00 5.00 1 0 1 0 0.02
3 1.861231e+07 2.020000e+13 129278480 5 7 NaN 42.390310 -71.001780 1 NaN ... NaN NaN NaN NaN NaN 5 0 5 0 NaN
4 7.990000e+17 2.020000e+13 193540040 2 2 NaN 42.390136 -71.000661 2 NaN ... 5.00 4.86 4.93 4.57 4.36 2 2 0 0 3.21

5 rows × 40 columns

we will remove unimportant columns and leave what we think that is important and effective¶

In [9]:
dc=df_object.loc[:,['host_location','host_response_rate','host_acceptance_rate','has_availability','host_neighbourhood','instant_bookable','neighbourhood_cleansed','room_type','price']]
In [10]:
dn=df_number.loc[:,['id','bedrooms','accommodates','number_of_reviews_l30d']]

Filling missing values¶

In [11]:
dc['host_location']=dc['host_location'].fillna(dc['host_location'].mode()[0])
In [12]:
dc['host_neighbourhood']=dc['host_neighbourhood'].fillna(dc['host_neighbourhood'].mode()[0])

Coverting some columns dtype from object to number¶

In [13]:
dc['price']=dc['price'].apply(lambda x:x[1:])
In [14]:
dc['price']=dc['price'].apply(lambda x:float(x.replace(',', '')))
In [15]:
dc['price']
Out[15]:
0       130.0
1       150.0
2        60.0
3        50.0
4       140.0
        ...  
3968    140.0
3969     80.0
3970     28.0
3971    110.0
3972    115.0
Name: price, Length: 3973, dtype: float64
In [16]:
dc['host_response_rate']=dc['host_response_rate'].fillna('000')
In [17]:
dc['host_response_rate']=dc['host_response_rate'].apply(lambda x:int(x[:-1]))
In [18]:
dc['host_acceptance_rate']=dc['host_acceptance_rate'].fillna('000')
In [19]:
dc['host_acceptance_rate']=dc['host_acceptance_rate'].apply(lambda x:int(x[:-1]))
In [20]:
dn['bedrooms'].fillna(round(dn['accommodates']/2),inplace=True)
In [21]:
dn['bedrooms']=dn['bedrooms'].replace(0,1)
In [22]:
dn['bedrooms']
Out[22]:
0       1.0
1       1.0
2       1.0
3       1.0
4       1.0
       ... 
3968    2.0
3969    1.0
3970    1.0
3971    2.0
3972    1.0
Name: bedrooms, Length: 3973, dtype: float64

Joining the sperated data¶

In [23]:
final_df=dn.join(dc)

Coverting some Catigorical columns to Numerical¶

In [24]:
d={'t':1,'f':0}
final_df['has_availability']=final_df['has_availability'].map(d)
final_df['instant_bookable']=final_df['instant_bookable'].map(d)
final_df['instant_bookable']
Out[24]:
0       0
1       0
2       0
3       0
4       1
       ..
3968    0
3969    0
3970    0
3971    0
3972    0
Name: instant_bookable, Length: 3973, dtype: int64
In [25]:
final_df.head()
Out[25]:
id bedrooms accommodates number_of_reviews_l30d host_location host_response_rate host_acceptance_rate has_availability host_neighbourhood instant_bookable neighbourhood_cleansed room_type price
0 5.216497e+07 1.0 2 0 Boston, MA 0 100 1 Harbor View / Orient Heights 0 East Boston Private room 130.0
1 5.107200e+07 1.0 4 7 Boston, MA 0 100 1 Harbor View / Orient Heights 0 East Boston Entire home/apt 150.0
2 3.344684e+07 1.0 1 0 Boston, MA 100 100 1 Cambridge 0 East Boston Private room 60.0
3 1.861231e+07 1.0 1 0 Boston, MA 100 100 1 East Boston 0 East Boston Private room 50.0
4 7.990000e+17 1.0 2 6 Boston, MA 100 99 1 Harbor View / Orient Heights 1 East Boston Entire home/apt 140.0
In [26]:
final_df.isna().sum()
Out[26]:
id                        0
bedrooms                  0
accommodates              0
number_of_reviews_l30d    0
host_location             0
host_response_rate        0
host_acceptance_rate      0
has_availability          0
host_neighbourhood        0
instant_bookable          0
neighbourhood_cleansed    0
room_type                 0
price                     0
dtype: int64

Exploratory Data Analysis (EDA)¶

In [27]:
final_df.describe().T
Out[27]:
count mean std min 25% 50% 75% max
id 3973.0 2.745824e+17 3.643551e+17 3781.0 26108847.0 48905210.0 6.850000e+17 9.190000e+17
bedrooms 3973.0 1.520765e+00 9.114270e-01 1.0 1.0 1.0 2.000000e+00 1.300000e+01
accommodates 3973.0 3.218978e+00 2.227805e+00 1.0 2.0 2.0 4.000000e+00 1.600000e+01
number_of_reviews_l30d 3973.0 1.129625e+00 2.047783e+00 0.0 0.0 0.0 2.000000e+00 1.600000e+01
host_response_rate 3973.0 8.501007e+01 3.309416e+01 0.0 94.0 100.0 1.000000e+02 1.000000e+02
host_acceptance_rate 3973.0 7.793179e+01 3.508290e+01 0.0 69.0 97.0 1.000000e+02 1.000000e+02
has_availability 3973.0 9.554493e-01 2.063411e-01 0.0 1.0 1.0 1.000000e+00 1.000000e+00
instant_bookable 3973.0 2.479235e-01 4.318615e-01 0.0 0.0 0.0 0.000000e+00 1.000000e+00
price 3973.0 2.283088e+02 2.679322e+02 0.0 100.0 175.0 2.750000e+02 1.000000e+04
In [28]:
final_df.describe(include='object').T
Out[28]:
count unique top freq
host_location 3973 121 Boston, MA 3056
host_neighbourhood 3973 111 Cambridge 563
neighbourhood_cleansed 3973 25 Dorchester 507
room_type 3973 4 Entire home/apt 2628
In [29]:
pd.DataFrame(final_df.apply(lambda col: len(col.unique())),columns=["Unique Values Count"])
Out[29]:
Unique Values Count
id 2847
bedrooms 9
accommodates 16
number_of_reviews_l30d 16
host_location 121
host_response_rate 37
host_acceptance_rate 65
has_availability 2
host_neighbourhood 111
instant_bookable 2
neighbourhood_cleansed 25
room_type 4
price 621
In [30]:
final_df.corr()
Out[30]:
id bedrooms accommodates number_of_reviews_l30d host_response_rate host_acceptance_rate has_availability instant_bookable price
id 1.000000 0.087348 0.080384 -0.009583 0.210719 0.174509 0.154715 0.063821 0.070566
bedrooms 0.087348 1.000000 0.854293 0.069038 0.011053 0.061651 0.044412 0.047362 0.416448
accommodates 0.080384 0.854293 1.000000 0.114910 0.033367 0.123876 0.056827 0.127256 0.435901
number_of_reviews_l30d -0.009583 0.069038 0.114910 1.000000 0.213164 0.288548 0.116749 0.034822 0.027294
host_response_rate 0.210719 0.011053 0.033367 0.213164 1.000000 0.809663 0.535689 0.057904 0.028932
host_acceptance_rate 0.174509 0.061651 0.123876 0.288548 0.809663 1.000000 0.467106 0.131609 0.107997
has_availability 0.154715 0.044412 0.056827 0.116749 0.535689 0.467106 1.000000 0.123980 0.050851
instant_bookable 0.063821 0.047362 0.127256 0.034822 0.057904 0.131609 0.123980 1.000000 0.133159
price 0.070566 0.416448 0.435901 0.027294 0.028932 0.107997 0.050851 0.133159 1.000000

We have to divide some columns into some ranges so that we can get the Relations¶

In [31]:
final_df['price_Group']= pd.cut(final_df['price'], bins=[-1,100,200,300,400,500,600,700,800,900,np.inf], labels=['<100.0', '100-200', '200-300', '300-400', '400-500','500-600','600-700','700-800','800-900','900+'])
final_df['price_Group'].isna().sum()
Out[31]:
0
In [32]:
final_df['response_Group']= pd.cut(final_df['host_response_rate'], bins=[-1,10,20,30,40,50,60,70,80,90,np.inf], labels=['<10', '10-20', '20-30', '30-40', '40-50','50-60','60-70','70-80','80-90','90+'])
final_df['response_Group'].isna().sum()
Out[32]:
0
In [33]:
final_df['accept_Group']= pd.cut(final_df['host_acceptance_rate'], bins=[-1,10,20,30,40,50,60,70,80,90,np.inf], labels=['<10', '10-20', '20-30', '30-40', '40-50','50-60','60-70','70-80','80-90','90+'])
final_df['accept_Group'].isna().sum()
Out[33]:
0
In [34]:
fig, axes = plt.subplots(1,4, figsize = (16,5))
idx = 0

for col in ['has_availability', 'instant_bookable', 'room_type', 'bedrooms']:
    sns.histplot(data = final_df[col], ax = axes[idx],)
    idx+=1
In [35]:
plt.hist(final_df['price'],bins=15,range=[0,1000])
plt.figure()
Out[35]:
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
In [36]:
final_df['has_availability'].value_counts().plot.pie(autopct='%1.1f%%')
Out[36]:
<AxesSubplot:ylabel='has_availability'>

The availability is almost always exist¶

In [37]:
final_df['instant_bookable'].value_counts().plot.pie(autopct='%1.1f%%')
Out[37]:
<AxesSubplot:ylabel='instant_bookable'>
In [38]:
final_df['room_type'].value_counts().plot(kind='bar')
Out[38]:
<AxesSubplot:>
In [39]:
final_df['bedrooms'].value_counts().plot(kind='bar')
Out[39]:
<AxesSubplot:>
In [40]:
final_df['accommodates'].value_counts().plot(kind='bar')
Out[40]:
<AxesSubplot:>
In [41]:
plt.figure(figsize=(10, 6))
sns.countplot(x='price_Group', hue='room_type', data=final_df)
plt.xticks(rotation='vertical')
plt.legend(loc='upper right')
plt.show()

All price Range have Entire home/apt & private rooms but private rooms is more in <1oo$ ,The shared rooms are just in low price range while ofcaurse Hotel rooms in middel and very high price range¶

In [42]:
plt.figure(figsize=(10, 7))
sns.countplot(x='price_Group', hue='bedrooms', data=final_df)
plt.xticks(rotation='vertical')
plt.legend( loc = 'upper right')
plt.show()

In Genral The more the appartment price the more the number of beds included¶

In [43]:
plt.figure(figsize=(10, 6))
sns.countplot(x='price_Group', hue='accept_Group', data=final_df)
plt.xticks(rotation='vertical')
plt.legend(loc='upper right')
plt.show()

The acceptance is almost always high while low acceptance rate is clear in low price maybe because of limited quality¶

In [44]:
plt.figure(figsize=(10, 6))
sns.countplot(x='price_Group', hue='response_Group', data=final_df)
plt.xticks(rotation='vertical')
plt.legend(loc='upper right')
plt.show()

The host response rate is almost always high while low host response rate is clear in low price maybe because of high order or demand rate¶

In [45]:
plt.figure(figsize=(10, 6))
sns.countplot(x='price_Group', hue='has_availability', data=final_df)
plt.xticks(rotation='vertical')
plt.legend(loc='upper right')
plt.show()

The availability is almost always exist while unavailability is clear in low price maybe because of high order or demand rate¶

In [46]:
plt.figure(figsize=(10, 6))
sns.countplot(x='price_Group', hue='instant_bookable', data=final_df)
plt.xticks(rotation='vertical')
plt.legend(loc='upper right')
plt.show()

The instant bookable is limited in low price range and equal in middel and high in high price range that is definitely because of Supply & Demand Rule¶

For More Clearification & Understanding There is definitely an important Feature [Location]¶

In [47]:
df['price']=df['price'].apply(lambda x:x[1:])
In [48]:
df['price']=df['price'].apply(lambda x:float(x.replace(',', '')))
In [49]:
df['price'].isna().sum()
Out[49]:
0
In [50]:
df['colur']= pd.cut(df['price'], bins=[0,100,200,300,400,500,600,700,800,900,np.inf], labels=['<100.0', '100-200', '200-300', '300-400', '400-500','500-600','600-700','700-800','800-900','900+'])
df['colur']=df['colur'].fillna('100-200')
df['colur'].isna().sum()
Out[50]:
0
In [51]:
df['colur'].value_counts()
Out[51]:
100-200    1291
<100.0     1034
200-300     846
300-400     365
400-500     168
500-600      96
900+         70
600-700      48
800-900      29
700-800      26
Name: colur, dtype: int64
In [52]:
import plotly.express as px

df['size'] = 22
fig = px.scatter_mapbox(df, 
                        lat='latitude', 
                        lon='longitude',
                        zoom=9,
                        size_max=1, # manually set largest size
                        color='colur',
                        hover_name="colur")                   
fig.update_layout(mapbox_style="open-street-map")

fig.show()

The Low price appartments are evry where in the City while The High price appartments are almost just in the Middel of THe City¶

Conclusions ( The Important Feature)¶

1- All price Range have Entire home/apt & private rooms but private rooms is more in <1oo$ ,The shared rooms are just in low price range while ofcaurse Hotel rooms in middel and very high price range¶

2- The acceptance is almost always high while low acceptance rate is clear in low price maybe because of limited quality¶

3- In Genral The more the appartment price the more the number of beds included¶

4- The Low price appartments are evry where in the City while The High price appartments are almost just in the Middel of THe City¶

In [ ]: